The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to...The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.展开更多
“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism ...“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.展开更多
Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecul...Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies.展开更多
LargeLanguageModels(LLMs)are increasingly appliedinthe fieldof code translation.However,existing evaluation methodologies suffer from two major limitations:(1)the high overlap between test data and pretraining corpora...LargeLanguageModels(LLMs)are increasingly appliedinthe fieldof code translation.However,existing evaluation methodologies suffer from two major limitations:(1)the high overlap between test data and pretraining corpora,which introduces significant bias in performance evaluation;and(2)mainstream metrics focus primarily on surface-level accuracy,failing to uncover the underlying factors that constrain model capabilities.To address these issues,this paper presents TCode(Translation-Oriented Code Evaluation benchmark)—a complexity-controllable,contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework.The dataset is carefully designed to control complexity along multiple dimensions—including syntactic nesting and expression intricacy—enabling both broad coverage and fine-grained differentiation of sample difficulty.This design supports precise evaluation of model capabilities across a wide spectrum of translation challenges.The proposed evaluation framework introduces a correlation-driven analysis mechanism based on static program features,enabling predictive modeling of translation success from two perspectives:Code Form Complexity(e.g.,code length and character density)and Semantic Modeling Complexity(e.g.,syntactic depth,control-flow nesting,and type system complexity).Empirical evaluations across representative LLMs—including Qwen2.5-72B and Llama3.3-70B—demonstrate that even state-of-the-art models achieve over 80% compilation success on simple samples,but their accuracy drops sharply below 40% on complex cases.Further correlation analysis indicates that Semantic Modeling Complexity alone is correlated with up to 60% of the variance in translation success,with static program features exhibiting nonlinear threshold effects that highlight clear capability boundaries.This study departs fromthe traditional accuracy-centric evaluation paradigm and,for the first time,systematically characterizes the capabilities of large languagemodels in translation tasks through the lens of programstatic features.The findings provide actionable insights for model refinement and training strategy development.展开更多
In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statist...In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.展开更多
Superoxide dismutase 1(SOD1)is a thermodynamically stable,zinc and copper binding homodimeric enzyme responsible for breaking down superoxide radicals.More than 200,mostly missense,mutations spread throughout the SOD1...Superoxide dismutase 1(SOD1)is a thermodynamically stable,zinc and copper binding homodimeric enzyme responsible for breaking down superoxide radicals.More than 200,mostly missense,mutations spread throughout the SOD1 gene are associated with the fatal neurodegenerative disease,amyotrophic lateral sclerosis(ALS).A unifying feature of ALS-associated SOD1 mutations is the destabilization of the SOD1 protein structure,increasing the propensity for misfolding and subsequent pathological aggregation.Post-mortem analysis of SOD1-associated ALS tissue shows the accumulation of misfolded SOD1 protein and ubiquitinated SOD1 inclusions within motor neurons.Misfolded SOD1 accumulation and aggregates are implicated in cellular dysfunction via a number of disparate but critical processes,including endoplasmic reticulum stress,oxidative damage,proteasome dysfunction,axonal transport abnormalities and synaptic dysfunction;culminating in motor neuron degeneration associated with ALS.展开更多
The lysosomal enzyme β-glucocerebrosidase(GCase) belongs to the family of glycosidases and hydrolyses the glycosphingolipid glucosylceramide(GluCer) into glucose and ceramide. The enzyme is of central importance for ...The lysosomal enzyme β-glucocerebrosidase(GCase) belongs to the family of glycosidases and hydrolyses the glycosphingolipid glucosylceramide(GluCer) into glucose and ceramide. The enzyme is of central importance for two pathologies:(1) the lysosomal storage disorder Gaucher's disease(GD) and(2) the neurodegenerative disorder Parkinson's disease(PD).展开更多
In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper propo...In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper proposes a Transformer-based multi-domain-aware mixture of experts model.To address the problem of domain feature differentiation,a mixture of experts(MoE)is introduced into attention to enhance the domain perception ability of the model,thereby improving the domain feature differentiation.To address the trade-off between domain feature distinction and cross-domain parameter sharing,we propose a domain-aware mixture of experts(DMoE).A domain-aware gating mechanism is introduced within the MoE module,simultaneously activating all domain experts to effectively blend domain feature distinction and cross-domain parameter sharing.A loss balancing function is then added to dynamically adjust the impact of the loss function on the expert distribution,enabling fine-tuning of the expert activation distribution to achieve a balance between domains.Experimental results on multiple Chinese-to-English and English-to-French datasets demonstrate that our proposed method significantly outperforms baseline models in both BLEU,chrF,and COMET metrics,validating its effectiveness in multi-domain neural machine translation.Further analysis of the probability distribution of expert activations shows that our method achieves remarkable results in both domain differentiation and cross-domain parameter sharing.展开更多
When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based ...When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based models offer strong baselines,they compromise syntactic awareness and the detection and man-agement of offensive content in cluttered,noisy,and informal text.In this paper,we present POSDEP-Offense-Trans,a multi-task NMT framework that combines Part-of-Speech(POS)and Dependency Parsing(DEP)methods with a robust offensive language classification module.Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms.The architecture incorporates a structure-aware contrastive loss that reinforces syntactic consistency and deploys auxiliary classification heads for POS tagging,dependency parsing,and multi-class offensive detection.The classifier for offensive words operates at both sentence and token levels and obtains guidance from syntactic features and formal finite automata rules that model offensive language structures-hate speech,profanity,sarcasm,and threats.Using this architecture,we construct a syntactically enriched,socially annotated corpus.Experimental results show improvements in translation quality,with a BLEU score of 33.5,UAS/LAS parsing accuracies of 92.4%and 90%,and a 4.5%Fl-score gain in offensive content detection compared with baseline POS+DEP+Offense models.Also,the proposed model achieved 92.3%in offensive content neutralization,as confirmed by ablation studies.This comprehensive English-Tamil NMT model that unifies syntactic modelling and ethical filtering-laying the groundwork for applications in social media moderation,hate speech mitigation,and policy-compliant multilingual content generation.展开更多
The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by the...The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by their interdisciplinary nature,regulatory constraints,and function-oriented definitions.Based on real-world translation examples,this paper analyzes the challenges in translating cosmetics ingredient terminology for international trade and proposes targeted translation strategies.The study aims to facilitate the global marketing of cosmetic products while ensuring consumers can accurately understand product ingredient information.展开更多
Background:An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer(SCLC).The systematic review aimed to summarize the characteristics of ferro...Background:An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer(SCLC).The systematic review aimed to summarize the characteristics of ferroptosis from its pathogenetic role to translational therapeutic implications in SCLC.Methods:This systematic review,registered in PROSPERO(CRD420251090058),followed PRISMA 2020 guidelines.Comprehensive research of PubMed,Scopus,and Web of Science was performed for studies published between January 2010 and July 2025 investigating ferroptosis mechanisms,genetic or pharmacological modulation,or molecular profiling in SCLC.Two reviewers independently performed data extraction and quality assessment.Results:Nineteen preclinical studies met the inclusion criteria.Key regulators included solute carrier family 7 member 11(SLC7A11),glutathione peroxidase 4(GPX4),ferroptosis suppressor protein 1(FSP1),and acyl-CoA synthetase long chain family member 4(ACSL4).The molecular subtypes of SCLC,achaete-scute homolog 1(ASCL1),neuronal differentiation 1(NEUROD1),POU class 2 homeobox 3(POU2F3),and Yes1 associated transcriptional regulator(YAP1)exhibit differential ferroptosis gene expressions,influencing therapeutic responsiveness.Non-neuroendocrine subtypes are more ferroptosis-prone,whereas neuroendocrine variants display enhanced antioxidant defenses.Ferroptosis induction also promotes immune activation through stimulator of interferon genes(STING)-mediated CD8+T-cell recruitment.Conclusions:Ferroptosis constitutes a promising therapeutic axis in SCLC.Integrating ferroptosis biomarkers into molecular stratification frameworks could refine patient selection and support precision oncology strategies,warranting further translational and clinical validation.展开更多
The sentence structure and syntax of the Mongolian heroic epic Jangar are diverse and rich in cultural connotations.Through the relentless efforts of generations of translators,Chinese readers can now appreciate this ...The sentence structure and syntax of the Mongolian heroic epic Jangar are diverse and rich in cultural connotations.Through the relentless efforts of generations of translators,Chinese readers can now appreciate this great heroic epic without hindrance.Studying the translation cases of epic lines of the Jangar holds significant importance for the translation of folk literature,including heroic epics.This paper takes the Chinese translations of the 70–chaptered edition of the epic Jangar as the research object,briefly discusses the characteristics of epic lines translation practices.The study reveals that translators employed relatively flexible methods when handling the original epic sentences,utilizing techniques such as altering sentence structures,transforming sentence components,integrating or splitting sentences,and adding or omitting words.展开更多
To promote the global dissemination of Chinese culture,the translation of Chinese classic literature has garnered widespread attention in the translation field.Literary translation criticism plays an essential role in...To promote the global dissemination of Chinese culture,the translation of Chinese classic literature has garnered widespread attention in the translation field.Literary translation criticism plays an essential role in the development of translation endeavors.This paper will analyze three versions of English translations of Shen Congwen’s novella Border Town using Reiss’s translation criticism model and summarize their performance in terms of formal equivalence and aesthetic effect,linguistic components,and extra-linguistic components.展开更多
This paper examines intra-national translation flows in Belgium over a 50-year period(1970–2020),focusing on how books circulate between two of the country’s official languages,Dutch and French,within a plurilingual...This paper examines intra-national translation flows in Belgium over a 50-year period(1970–2020),focusing on how books circulate between two of the country’s official languages,Dutch and French,within a plurilingual literary space.By applying a“Big Translation History”approach,the study reveals the influence of language status,location,genre,actor roles,and publishing practices on translation dynamics.Notably,Belgian publishers control a significant portion of the market for comics and children’s literature,demonstrating a distinct national production culture for these genres,whereas a prestigious genre like the novel is to a large extent in the hands of dominant neighboring states that share the same language(France and the Netherlands).The paper nuances the traditional center-periphery model in Translation Studies and suggests that intra-national translation flows are influenced not only by global language hierarchies but also by other factors such as genre and publishing location,providing a richer understanding of cultural exchange in multilingual states.展开更多
This study investigates how artificial intelligence(AI)reshapes China’s translation industry through technological innovation,industrial restructuring,and talent demand evolution between 2015 and 2025.Analyzing empir...This study investigates how artificial intelligence(AI)reshapes China’s translation industry through technological innovation,industrial restructuring,and talent demand evolution between 2015 and 2025.Analyzing empirical data from 500 translation enterprises(including 320 small and medium-sized enterprises[SMEs]and 180 large-scale corporations),20 universities offering translation programs,and 12 key policy documents from China’s Ministry of Education(MOE),Cyberspace Administration of China(CAC),and Translators Association of China(TAC),we identify three distinct evolutionary phases of AI translation in China:rule-based systems(2010-2017),neural machine translation(NMT,2018-2022),and large language model(LLM)-driven intelligent adaptation(2023-2025).Key findings reveal that AI boosts translation efficiency by 40-60%through human-machine collaboration-reducing technical document processing time from 72 hours to 18 hours for complex BRI infrastructure blueprints-and expands service coverage to 34 languages(including 12 non-common languages critical for BRI,such as Kazakh,Swahili,and Urdu).However,AI also exacerbates talent mismatch:by 2025,China faced a shortage of over 50,000“AI-translation compound professionals”(individuals proficient in both translation and AI tool operation, ;corpus management,or LLM fine-tuning),particularly in high-stakes fields like legal contract translation and medical document localization.Ethical risks further challenge sustainability:a cross-sectional survey of 500 enterprises conducted in 2024-2025 found that 45%of SMEs violated data privacy regulations(e.g.,using client confidential documents to train LLMs without consent),and 30%of LLM-generated literary translations infringed copyrights.Policy recommendations include aligning with China’s New Generation AI Development Plan(2024-2030)to accelerate domain-specific standardization(e.g.,mandatory certification for legal and medical AI translation tools)and integrating AI literacy into translation curricula(e.g.,mandatory“LLM Operation and Quality Assessment”courses in all undergraduate translation programs by 2026).展开更多
文摘The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.
文摘“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.
基金Ministry of Research,Innovation and Digitization,CCCDI-UEFISCDI,Grant/Award Number:PN-IV-P7-7.1-PED-2024-1578,within PNCDI Ⅳ.
文摘Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies.
文摘LargeLanguageModels(LLMs)are increasingly appliedinthe fieldof code translation.However,existing evaluation methodologies suffer from two major limitations:(1)the high overlap between test data and pretraining corpora,which introduces significant bias in performance evaluation;and(2)mainstream metrics focus primarily on surface-level accuracy,failing to uncover the underlying factors that constrain model capabilities.To address these issues,this paper presents TCode(Translation-Oriented Code Evaluation benchmark)—a complexity-controllable,contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework.The dataset is carefully designed to control complexity along multiple dimensions—including syntactic nesting and expression intricacy—enabling both broad coverage and fine-grained differentiation of sample difficulty.This design supports precise evaluation of model capabilities across a wide spectrum of translation challenges.The proposed evaluation framework introduces a correlation-driven analysis mechanism based on static program features,enabling predictive modeling of translation success from two perspectives:Code Form Complexity(e.g.,code length and character density)and Semantic Modeling Complexity(e.g.,syntactic depth,control-flow nesting,and type system complexity).Empirical evaluations across representative LLMs—including Qwen2.5-72B and Llama3.3-70B—demonstrate that even state-of-the-art models achieve over 80% compilation success on simple samples,but their accuracy drops sharply below 40% on complex cases.Further correlation analysis indicates that Semantic Modeling Complexity alone is correlated with up to 60% of the variance in translation success,with static program features exhibiting nonlinear threshold effects that highlight clear capability boundaries.This study departs fromthe traditional accuracy-centric evaluation paradigm and,for the first time,systematically characterizes the capabilities of large languagemodels in translation tasks through the lens of programstatic features.The findings provide actionable insights for model refinement and training strategy development.
基金supported by the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202307)the Basic Research Fund of CAMS(No.2023Z016)+1 种基金the National Natural Scientific Foundation of China(No.42275037)the Jiangsu Collaborative Innovation Center for Climate Change。
文摘In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.
基金Motor Neuron Disease Research Australia in the form of a Bill Gole Postdoctoral Fellowship(PDF2307)FightMND in the form of Drug Development Grants(DDG-159 and DDG137 to JSL)。
文摘Superoxide dismutase 1(SOD1)is a thermodynamically stable,zinc and copper binding homodimeric enzyme responsible for breaking down superoxide radicals.More than 200,mostly missense,mutations spread throughout the SOD1 gene are associated with the fatal neurodegenerative disease,amyotrophic lateral sclerosis(ALS).A unifying feature of ALS-associated SOD1 mutations is the destabilization of the SOD1 protein structure,increasing the propensity for misfolding and subsequent pathological aggregation.Post-mortem analysis of SOD1-associated ALS tissue shows the accumulation of misfolded SOD1 protein and ubiquitinated SOD1 inclusions within motor neurons.Misfolded SOD1 accumulation and aggregates are implicated in cellular dysfunction via a number of disparate but critical processes,including endoplasmic reticulum stress,oxidative damage,proteasome dysfunction,axonal transport abnormalities and synaptic dysfunction;culminating in motor neuron degeneration associated with ALS.
基金supported by the Michael J Fox Foundation (to PA and FZ)。
文摘The lysosomal enzyme β-glucocerebrosidase(GCase) belongs to the family of glycosidases and hydrolyses the glycosphingolipid glucosylceramide(GluCer) into glucose and ceramide. The enzyme is of central importance for two pathologies:(1) the lysosomal storage disorder Gaucher's disease(GD) and(2) the neurodegenerative disorder Parkinson's disease(PD).
基金supported by the NationalNatural Science Foundation of China(U2004163)Key Research and Development Program of Henan Province(No.251111211200).
文摘In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper proposes a Transformer-based multi-domain-aware mixture of experts model.To address the problem of domain feature differentiation,a mixture of experts(MoE)is introduced into attention to enhance the domain perception ability of the model,thereby improving the domain feature differentiation.To address the trade-off between domain feature distinction and cross-domain parameter sharing,we propose a domain-aware mixture of experts(DMoE).A domain-aware gating mechanism is introduced within the MoE module,simultaneously activating all domain experts to effectively blend domain feature distinction and cross-domain parameter sharing.A loss balancing function is then added to dynamically adjust the impact of the loss function on the expert distribution,enabling fine-tuning of the expert activation distribution to achieve a balance between domains.Experimental results on multiple Chinese-to-English and English-to-French datasets demonstrate that our proposed method significantly outperforms baseline models in both BLEU,chrF,and COMET metrics,validating its effectiveness in multi-domain neural machine translation.Further analysis of the probability distribution of expert activations shows that our method achieves remarkable results in both domain differentiation and cross-domain parameter sharing.
文摘When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based models offer strong baselines,they compromise syntactic awareness and the detection and man-agement of offensive content in cluttered,noisy,and informal text.In this paper,we present POSDEP-Offense-Trans,a multi-task NMT framework that combines Part-of-Speech(POS)and Dependency Parsing(DEP)methods with a robust offensive language classification module.Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms.The architecture incorporates a structure-aware contrastive loss that reinforces syntactic consistency and deploys auxiliary classification heads for POS tagging,dependency parsing,and multi-class offensive detection.The classifier for offensive words operates at both sentence and token levels and obtains guidance from syntactic features and formal finite automata rules that model offensive language structures-hate speech,profanity,sarcasm,and threats.Using this architecture,we construct a syntactically enriched,socially annotated corpus.Experimental results show improvements in translation quality,with a BLEU score of 33.5,UAS/LAS parsing accuracies of 92.4%and 90%,and a 4.5%Fl-score gain in offensive content detection compared with baseline POS+DEP+Offense models.Also,the proposed model achieved 92.3%in offensive content neutralization,as confirmed by ablation studies.This comprehensive English-Tamil NMT model that unifies syntactic modelling and ethical filtering-laying the groundwork for applications in social media moderation,hate speech mitigation,and policy-compliant multilingual content generation.
基金funded by 2025 Graduate Teaching Construction Project of USST.
文摘The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by their interdisciplinary nature,regulatory constraints,and function-oriented definitions.Based on real-world translation examples,this paper analyzes the challenges in translating cosmetics ingredient terminology for international trade and proposes targeted translation strategies.The study aims to facilitate the global marketing of cosmetic products while ensuring consumers can accurately understand product ingredient information.
基金supported by Regione Autonoma della Sardegna,pursuant to Regional Law 07 August 2007,n.7“Promotion of Scientific Research and Technological Innovation in Sardinia—UGOV Project RAS_CRP2023 CARRULilt Nazionale—5 per mille Program for the year 2022,LILT 2023 scientific-health research call,Number:LILT—Protocol number 2024U0001294 of 29.03.2024。
文摘Background:An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer(SCLC).The systematic review aimed to summarize the characteristics of ferroptosis from its pathogenetic role to translational therapeutic implications in SCLC.Methods:This systematic review,registered in PROSPERO(CRD420251090058),followed PRISMA 2020 guidelines.Comprehensive research of PubMed,Scopus,and Web of Science was performed for studies published between January 2010 and July 2025 investigating ferroptosis mechanisms,genetic or pharmacological modulation,or molecular profiling in SCLC.Two reviewers independently performed data extraction and quality assessment.Results:Nineteen preclinical studies met the inclusion criteria.Key regulators included solute carrier family 7 member 11(SLC7A11),glutathione peroxidase 4(GPX4),ferroptosis suppressor protein 1(FSP1),and acyl-CoA synthetase long chain family member 4(ACSL4).The molecular subtypes of SCLC,achaete-scute homolog 1(ASCL1),neuronal differentiation 1(NEUROD1),POU class 2 homeobox 3(POU2F3),and Yes1 associated transcriptional regulator(YAP1)exhibit differential ferroptosis gene expressions,influencing therapeutic responsiveness.Non-neuroendocrine subtypes are more ferroptosis-prone,whereas neuroendocrine variants display enhanced antioxidant defenses.Ferroptosis induction also promotes immune activation through stimulator of interferon genes(STING)-mediated CD8+T-cell recruitment.Conclusions:Ferroptosis constitutes a promising therapeutic axis in SCLC.Integrating ferroptosis biomarkers into molecular stratification frameworks could refine patient selection and support precision oncology strategies,warranting further translational and clinical validation.
文摘The sentence structure and syntax of the Mongolian heroic epic Jangar are diverse and rich in cultural connotations.Through the relentless efforts of generations of translators,Chinese readers can now appreciate this great heroic epic without hindrance.Studying the translation cases of epic lines of the Jangar holds significant importance for the translation of folk literature,including heroic epics.This paper takes the Chinese translations of the 70–chaptered edition of the epic Jangar as the research object,briefly discusses the characteristics of epic lines translation practices.The study reveals that translators employed relatively flexible methods when handling the original epic sentences,utilizing techniques such as altering sentence structures,transforming sentence components,integrating or splitting sentences,and adding or omitting words.
文摘To promote the global dissemination of Chinese culture,the translation of Chinese classic literature has garnered widespread attention in the translation field.Literary translation criticism plays an essential role in the development of translation endeavors.This paper will analyze three versions of English translations of Shen Congwen’s novella Border Town using Reiss’s translation criticism model and summarize their performance in terms of formal equivalence and aesthetic effect,linguistic components,and extra-linguistic components.
文摘This paper examines intra-national translation flows in Belgium over a 50-year period(1970–2020),focusing on how books circulate between two of the country’s official languages,Dutch and French,within a plurilingual literary space.By applying a“Big Translation History”approach,the study reveals the influence of language status,location,genre,actor roles,and publishing practices on translation dynamics.Notably,Belgian publishers control a significant portion of the market for comics and children’s literature,demonstrating a distinct national production culture for these genres,whereas a prestigious genre like the novel is to a large extent in the hands of dominant neighboring states that share the same language(France and the Netherlands).The paper nuances the traditional center-periphery model in Translation Studies and suggests that intra-national translation flows are influenced not only by global language hierarchies but also by other factors such as genre and publishing location,providing a richer understanding of cultural exchange in multilingual states.
文摘This study investigates how artificial intelligence(AI)reshapes China’s translation industry through technological innovation,industrial restructuring,and talent demand evolution between 2015 and 2025.Analyzing empirical data from 500 translation enterprises(including 320 small and medium-sized enterprises[SMEs]and 180 large-scale corporations),20 universities offering translation programs,and 12 key policy documents from China’s Ministry of Education(MOE),Cyberspace Administration of China(CAC),and Translators Association of China(TAC),we identify three distinct evolutionary phases of AI translation in China:rule-based systems(2010-2017),neural machine translation(NMT,2018-2022),and large language model(LLM)-driven intelligent adaptation(2023-2025).Key findings reveal that AI boosts translation efficiency by 40-60%through human-machine collaboration-reducing technical document processing time from 72 hours to 18 hours for complex BRI infrastructure blueprints-and expands service coverage to 34 languages(including 12 non-common languages critical for BRI,such as Kazakh,Swahili,and Urdu).However,AI also exacerbates talent mismatch:by 2025,China faced a shortage of over 50,000“AI-translation compound professionals”(individuals proficient in both translation and AI tool operation, ;corpus management,or LLM fine-tuning),particularly in high-stakes fields like legal contract translation and medical document localization.Ethical risks further challenge sustainability:a cross-sectional survey of 500 enterprises conducted in 2024-2025 found that 45%of SMEs violated data privacy regulations(e.g.,using client confidential documents to train LLMs without consent),and 30%of LLM-generated literary translations infringed copyrights.Policy recommendations include aligning with China’s New Generation AI Development Plan(2024-2030)to accelerate domain-specific standardization(e.g.,mandatory certification for legal and medical AI translation tools)and integrating AI literacy into translation curricula(e.g.,mandatory“LLM Operation and Quality Assessment”courses in all undergraduate translation programs by 2026).
